Báo cáo khoa học: "Automatic Acquisition of Named Entity Tagged Corpus from World Wide Web" pot

Automatic Acquisition of Named Entity Tagged Corpus from World Wide Web Joohui An Dept. of CSE POSTECH Pohang, Korea 790-784 minnie@postech.ac.kr Seungwoo Lee Dept. of CSE POSTECH Pohang, Korea 790-784 pinesnow@postech.ac.kr Gary Geunbae Lee Dept. of CSE POSTECH Pohang, Korea 790-784 gblee@postech.ac.kr Abstract In this paper, we present a method that automatically constructs a Named En- tity (NE) tagged corpus from the web to be used for learning of Named En- tity Recognition systems. We use an NE list and an web search engine to col- lect web documents which contain the NE instances. The documents are refined through sentence separation and text re- finement procedures and NE instances are finally tagged with the appropriate NE cat- egories. Our experiments demonstrates that the suggested method can acquire enough NE tagged corpus equally useful to the manually tagged one without any human intervention. 1 Introduction Current trend in Named Entity Recognition (NER) is to apply machine learning approach, which is more attractive because it is trainable and adaptable, and subsequently the porting of a machine learning sys- tem to another domain is much easier than that of a rule-based one. Various supervised learning meth- ods for Named Entity (NE) tasks were successfully applied and have shown reasonably satisfiable per- formance.((Zhou and Su, 2002)(Borthwick et al., 1998)(Sassano and Utsuro, 2000)) However, most of these systems heavily rely on a tagged corpus for training. For a machine learning approach, a large corpus is required to circumvent the data sparseness problem, but the dilemma is that the costs required to annotate a large training corpus are non-trivial. In this paper, we suggest a method that automati- cally constructs an NE tagged corpus from the web to be used for learning of NER systems. We use an NE list and an web search engine to collect web doc- uments which contain the NE instances. The doc- uments are refined through the sentence separation and text refinement procedures and NE instances are finally annotated with the appropriate NE categories. This automatically tagged corpus may have lower quality than the manually tagged ones but its size can be almost infinitely increased without any hu- man efforts. To verify the usefulness of the con- structed NE tagged corpus, we apply it to a learn- ing of NER system and compare the results with the manually tagged corpus. 2 Automatic Acquisition of an NE Tagged Corpus We only focus on the three major NE categories (i.e., person, organization and location) because others are relatively easier to recognize and these three cat- egories actually suffer from the shortage of an NE tagged corpus. Various linguistic information is already held in common in written form on the web and its quantity is recently increasing to an almost unlimited extent. The web can be regarded as an infinite language re- source which contains various NE instances with di- verse contexts. It is the key idea that automatically marks such NE instances with appropriate category labels using pre-compiled NE lists. However, there should be some general and language-specific con- Web documents W1 W2 W3 … URL1 URL2 URL3 … Web search engine Web robot Sentence separator Text refinement S1 S2 S3 … 1.html 2.html … 1.ans 2.ans … NE list Web page URL Separated sentences Refined sentences NE tag generation S1(t) S2(t) S3(t) … NE tagged corpus Figure 1: Automatic generation of NE tagged corpus from the web siderations in this marking process because of the word ambiguity and boundary ambiguity of NE in- stances. To overcome these ambiguities, the auto- matic generation process of NE tagged corpus con- sists of four steps. The process first collects web documents using a web search engine fed with the NE entries and secondly segments them into sen- tences. Next, each sentence is refined and filtered out by several heuristics. An NE instance in each sentence is finally tagged with an appropriate NE category label. Figure 1 explains the entire proce- dure to automatically generate NE tagged corpus. 2.1 Collecting Web Documents It is not appropriate for our purpose to randomly col- lect documents from the web. This is because not all web documents actually contain some NE instances and we also do not have the list of all NE instances occurring in the web documents. We need to col- lect the web documents which necessarily contain at least one NE instance and also should know its category to automatically annotate it. This can be accomplished by using a web search engine queried with pre-compiled NE list. As queries to a search engine, we used the list of Korean Named Entities composed of 937 per- son names, 1,000 locations and 1,050 organizations. Using a Part-of-Speech dictionary, we removed am- biguous entries which are not proper nouns in other contexts to reduce errors of automatic annotation. For example, ‘E¶(kyunggi, Kyunggi/business con- ditions/a game)’ is filtered out because it means a lo- cation (proper noun) in one context, but also means business conditions or a game (common noun) in other contexts. By submitting the NE entries as queries to a search engine 1 , we obtained the max- imum 500 of URL’s for each entry. Then, a web robot visits the web sites in the URL list and fetches the corresponding web documents. 2.2 Splitting into Sentences Features used in the most NER systems can be clas- sified into two groups according to the distance from a target NE instance. The one includes internal fea- tures of NE itself and context features within a small word window or sentence boundary and the other in- cludes name alias and co-reference information be- yond a sentence boundary. In fact, it is not easy to extract name alias and co-reference information di- rectly from manually tagged NE corpus and needs additional knowledge or resources. This leads us to focus on automatic annotation in sentence level, not document level. Therefore, in this step, we split the texts of the collected documents into sentences by (Shim et al., 2002) and remove sentences without target NE instances. 2.3 Refining the Web Texts The collected web documents may include texts ac- tually matched by mistake, because most web search engines for Korean use n-gram, especially, bi-gram matching. This leads us to refine the sentences to ex- clude these erroneous matches. Sentence refinement is accomplished by three different processes: sep- aration of functional words, segmentation of com- pound nouns, and verification of the usefulness of the extracted sentences. An NE is often concatenated with more than one josa, a Korean functional word, to compose a Korean word. Therefore we need to separate the functional words from an NE instance to detect the boundary of the NE instance and this is achieved by a part-of-speech tagger, POSTAG, which can detect unknown words (Lee et al., 2002). The separation of functional words gives us another benefit that we can resolve the ambiguities between an NE and a common noun plus functional words 1 We used Empas (http://www.empas.com) Person Location Organization Training Automatic 29,042 37,480 2,271 Manual 1,014 724 1,338 Test Manual 102 72 193 Table 1: Corpus description (number of NE’s) (Au- tomatic: Automatically annotated corpus, Manual: Manually annotated corpus and filter out erroneous matches. For example, ‘E¶ê(kyunggi-do)’ can be interpreted as either ‘E¶ê(Kyunggi Province)’ or ‘E¶+ê(a game also)’ according to its context. We can remove the sentence containing the latter case. A josa-separated Korean word can be a com- pound noun which only contains a target NE as a substring. This requires us to segment the compound noun into several correct single nouns to match with the target NE. If the segmented single nouns are not matched with a target NE, the sentence can be fil- tered out. For example, we try to search for an NE entry, ‘¶Á(Fin.KL, a Korean singer group)’ and may actually retrieve sentences including ‘˚¶Á ě(surfing club)’. The compound noun, ‘˚¶Áě’, can be divided into ‘˚¶(surfing)’ and ‘Áě(club)’ by a compound-noun segmenting method (Yun et al., 1997). Since both ‘˚¶’ and ‘Áě’ are not matched with our target NE, ‘¶Á’, we can delete the sentences. Although a sentence has a correct tar- get NE, if it does not have context information, it is not useful as an NE tagged corpus. We also removed such sentences. 2.4 Generating an NE tagged corpus The sentences selected by the refining process ex- plained in previous section are finally annotated with the NE label. We acquired the NE tagged corpus in- cluding 68,793 NE instances through this automatic annotation process. We can annotate only one NE instance per sentence but almost infinitely increase the size of the corpus because the web provides un- limited data and our process is fully automatic. 3 Experimental Results 3.1 Usefulness of the Automatically Tagged Corpus For effectiveness of the learning, both the size and the accuracy of the training corpus are important. Training corpus Precision Recall F-measure Seeds only 84.13 42.91 63.52 Manual 80.21 86.11 83.16 Automatic 81.45 85.41 83.43 Manual + Automatic 82.03 85.94 83.99 Table 2: Performance of the decision list learning Generally, the accuracy of automatically created NE tagged corpus is worse than that of hand-made cor- pus. Therefore, it is important to examine the useful- ness of our automatically tagged corpus compared to the manual corpus. We separately trained the de- cision list learning features using the automatically annotated corpus and hand-made one, and compared the performances. Table 1 shows the details of the corpus used in our experiments. 2 Through the results in Table 2, we can verify that the performance with the automatic corpus is supe- rior to that with only the seeds and comparable to that with the manual corpus.Moreover, the domain of the manual training corpus is same with that of the test corpus, i.e., news and novels, while the do- main of the automatic corpus is unlimited as in the web. This indicates that the performance with the automatic corpus should be regarded as much higher than that with the manual corpus because the per- formance generally gets worse when we apply the learned system to different domains from the trained ones. Also, the automatic corpus is pretty much self- contained since the performance does not gain much though we use both the manual corpus and the auto- matic corpus for training. 3.2 Size of the Automatically Tagged Corpus As another experiment, we tried to investigate how large automatic corpus we should generate to get the satisfiable performance. We measured the perfor- mance according to the size of the automatic cor- pus. We carried out the experiment with the deci- sion list learning method and the result is shown in Table 3. Here, 5% actually corresponds to the size of the manual corpus. When we trained with that size of the automatic corpus, the performance was very low compared to the performance of the manual cor- pus. The reason is that the automatic corpus is com- 2 We used the manual corpus used in Seon et al. (2001) as training and test data. Corpus size (words) Precision Recall F-measure 90,000 (5%) 72.43 6.94 39.69 448,000 (25%) 73.17 41.66 57.42 902,000 (50%) 75.32 61.53 68.43 1,370,000 (75%) 78.23 77.19 77.71 1,800,000 (100%) 81.45 85.41 83.43 Table 3: Performance according to the corpus size Corpus size (words) Precision Recall F-measure 700,000 79.41 81.82 80.62 1,000,000 82.86 85.29 84.08 1,200,000 83.81 86.27 85.04 1,300,000 83.81 86.27 85.04 Table 4: Saturation point of the performance for ‘person’ category posed of the sentences searched with fewer named entities and therefore has less lexical and contextual information than the same size of the manual cor- pus. However, the automatic generation has a big merit that the size of the corpus can be increased al- most infinitely without much cost. From Table 3, we can see that the performance is improved as the size of the automatic corpus gets increased. As a result, the NER system trained with the whole au- tomatic corpus outperforms the NER system trained with the manual corpus. We also conducted an experiment to examine the saturation point of the performance according to the size of the automatic corpus. This experiment was focused on only ‘person’ category and the result is shown in Table 4. In the case of ‘person’ category, we can see that the performance does not increase any more when the corpus size exceeds 1.2 million words. 4 Conclusions In this paper, we presented a method that automat- ically generates an NE tagged corpus using enor- mous web documents. We use an internet search en- gine with an NE list to collect web documents which may contain the NE instances. The web documents are segmented into sentences and refined through sentence separation and text refinement procedures. The sentences are finally tagged with the NE cat- egories. We experimentally demonstrated that the suggested method could acquire enough NE tagged corpus equally useful to the manual corpus without any human intervention. In the future, we plan to ap- ply more sophisticated natural language processing schemes for automatic generation of more accurate NE tagged corpus. Acknowledgements This research was supported by BK21 program of Korea Ministry of Education and MOCIE strategic mid-term funding through ITEP. References Andrew Borthwick, John Sterling, Eugene Agichtein, and Ralph Grishman. 1998. Exploiting Diverse Knowledge Sources via Maximum Entropy in Named Entity Recognition. In Proceedings of the Sixth Work- shop on Very Large Corpora, pages 152–160, New Brunswick, New Jersey. Association for Computa- tional Linguistics. Gary Geunbae Lee, Jeongwon Cha, and Jong-Hyeok Lee. 2002. Syllable Pattern-based Unknown Mor- pheme Segmentation and Estimation for Hybrid Part- Of-Speech Tagging of Korean. Computational Lin- guistics, 28(1):53–70. Manabu Sassano and Takehito Utsuro. 2000. Named Entity Chunking Techniques in Supervised Learning for Japanese Named Entity Recognition. In Proceed- ings of the 18th International Conference on Compu- tational Linguistics (COLING 2000), pages 705–711, Germany. Choong-Nyoung Seon, Youngjoong Ko, Jeong-Seok Kim, and Jungyun Seo. 2001. Named Entity Recog- nition using Machine Learning Methods and Pattern- Selection Rules. In Proceedings of the Sixth Natural Language Processing Pacific Rim Symposium, pages 229–236, Tokyo, Japan. Junhyeok Shim, Dongseok Kim, Jeongwon Cha, Gary Geunbae Lee, and Jungyun Seo. 2002. Multi- strategic Integrated Web Document Pre-processing for Sentence and Word Boundary Detection. Information Processing and Management, 38(4):509–527. Bo-Hyun Yun, Min-Jeung Cho, and Hae-Chang Rim. 1997. Segmenting Korean Compound Nouns using Statistical Information and a Preference Rule. Jour- nal of Korean Information Science Society, 24(8):900– 909. GuoDong Zhou and Jian Su. 2002. Named Entity Recognition using an HMM-based Chunk Tagger. In Proceedings of the 40th Annual Meeting of the As- sociation for Computational Linguistics (ACL), pages 473–480, Philadelphia, USA. . Automatic Acquisition of Named Entity Tagged Corpus from World Wide Web Joohui An Dept. of CSE POSTECH Pohang, Korea 790-784 minnie@postech.ac.kr Seungwoo. of the con- structed NE tagged corpus, we apply it to a learn- ing of NER system and compare the results with the manually tagged corpus. 2 Automatic Acquisition